Journal of Investigative DermatologyCell BiologyPeer-reviewed journal article
Spatiotemporal Single-Cell Roadmap of Human Skin Wound Healing
Z. Liu, Xiaowei BianiD, Lihua Luo, Åsa K. BjörklundiD, Li LiiD, Letian ZhangiD, Yongjian CheniD, Lei GuoiD, Juan Gao, 曹春艳, Jiating WangiD, Wei HeiD, Yunting Xiao, Liping Zhu, Karl AnnusveriD, Nusayhah Hudaa GopeeiD, Daniela Basurto-LozadaiD, David HorsfalliD, Clare L. BennettiD, Maria KasperiD, Muzlifah Haniffa, Pehr SommariD, Dongqing Li, Ning Xu Landén
Abstract
Wound healing is vital for human health, yet the details of cellular dynamics and coordination in human wound repair remain largely unexplored. To address this, we conducted single-cell and spatial transcriptomics analyses on human skin and acute wound tissues through inflammation, proliferation, and remodeling phases of wound repair from the same individuals, monitoring the cellular and molecular dynamics of human skin wound healing at an unprecedented spatiotemporal resolution. This singular roadmap reveals the cellular architecture of the wound margin and identifies FOSL1 as a critical driver of re-epithelialization. It shows that pro-inflammatory macrophages and fibroblasts sequentially support keratinocyte migration like a relay race across different healing stages. Comparison with single-cell data from venous and diabetic foot ulcers uncovers a link between failed keratinocyte migration and impaired inflammatory response in chronic wounds. Additionally, comparing human and mouse acute wound transcriptomes underscores the indispensable value of this roadmap in bridging basic research with clinical innovations. HighlightsO_LIA spatiotemporal atlas to explore human in vivo gene expression across wound healing C_LIO_LIWound margin architecture unveils a model for human re-epithelialization C_LIO_LIDistinct healing challenges in venous ulcers and diabetic foot ulcers C_LIO_LIUnique human healing traits emerge in cellular heterogeneity and gene expression C_LI
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: cell-cell communication
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 263
- FWCI
- 86.34
- Field percentile
- top 1%
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